- Email: [email protected]
Computer simulation in healthcare decision making
{:~:rnlaul:ilO-m irtck,liatrlal
onilinilalr'irtil
PERGAMON
Computers & Industrial Engineering 37 (1999) 235-238
COMPUTER SIMULATION IN HEALTHCARE DECISION MAKING Tillal Eldabi Ray J. Paul Simon J. E. Taylor Centre for Applied Simulation Modelling Department of Information Systems and Computing Brunel University, Uxbridge Middlesex UB8 3PH ABSTRACT This paper presents simulation modelling as a decision support technique and suggests that it can be a useful for understanding problems related to health care. The paper shows that simulation may not be regarded as tool for deriving solutions to certain problems. In fact simulation is better suited for understanding the problem and enhancing systematic debate between the problem owners. The paper also demonstrates the usefulness of combining different software to provide a comprehensive tailor-made package (ABCSim). The example used is based on modelling a randomised clinical trial for Adjuvant Breast Cancer. © 1999 Elsevier Science Ltd. All rights reserved. Keywords: simulation modelling, decision making, clinical trials. INTRODUCTION The convoluted nature of health care systems requires managers to use sophisticated decision support techniques. The cost effective allocation of resources is likely to become one of the dominant issues in healthcare as it is in other fields. This article gives an example of how simulation modelling can facilitate informed debate between interested parties and demonstrate the consequences of different possible courses of action. The example discusses ABCSim, a simulation package for health economists with the aim of supporting the economic evaluation of the Adjuvant Breast Cancer (ABC) trial. The trial is a collaborative randomised clinical trial which was in progress in the UK. The principle aims of this trial were: •
•
to determine the value of adding cytotoxic chemotherapy and/or ovarian suppression to prolonged adjuvant tamoxifen in order to treat pre/perimenopausal women with early breast cancer; and to determine the value of adding cytotoxic chemotherapy to prolonged adjuvant tamoxifen in order to treat post menopausal women with early breast cancer.
The choice of treatment is determined by the particular condition of the patient or randomly determined from a series of suitable alternatives. It was proposed that the economic evaluation was to be performed by comparing the additional resource use with the survival gains and quality of life effects of the treatments combinations. However, the huge volume of data that the clinical 0360-8352/99 - see front matter © 1999 Elsevier Science Ltd. All rights reserved. PII: S0360-8352(99)00063-7
236
Proceedings of the 24th International Conference on Computers and Industrial Engineering
trials would generate made this task difficult. Next section provides a discussion about the need and use of modelling in such clinical trials to deal with the data problem. The section that follows presents the stages of modelling followed in this project. The verification and validation process is also visited. The article then gives a brief overview of ABCSim package. M O D E L L I N G THE ECONOMIC FACTORS The main role of simulation in this project is to model the perceived relationships between the economic factors in the treatment of adjuvant breast cancer with respect to reducing the amount of data collection required. Experimentation with the package would then be used to attempt to identify the important variables for data collection and examining the different responses to changes of configurations. The method used in modelling consists of three stages. These stages are problem structuring, modelling, and implementation. Problem structuring attempts to identify the problems in the system under investigation and the approaches that can be used to deal with them. Modelling is the heart of the project and involves the development of, and experimentation with, the computer model. Implementation attempts to put the outcomes of the study into practice. Pidd (1996) identifies the outcomes of these stages as being tangible recommendations or increased insight or understanding. At the first stage of modelling the trial, Activity Cycle Diagram (ACD) was used to generate the basic conceptual structure of the model (Paul and Balmer, 1993). Simul8, a simulation package, was used to generate the computer model based on the ACD model. The choice of the package was based on cost, ease of use and the ease of communicating with other programming tools for building a user interface. The package has special routines to communicate with Visual Basic, which was used for building ABCSim. The design of the interface is based on the user requirements and specification in order to make it easier for them to experiment with model. In other words, Simul8 is used for the modelling process whilst ABCSim is used for input and output manipulation by the users, health economists in this case. VALIDATION AND VERIFICATION The model covers a wide range of input variables that can be split broadly into three distinct categories: incidences and durations, costs, and quality of adjusted life years (QALY's). To date, the study has concentrated on collecting robust data for the validation of the incidence and duration and cost variable categories. The incidence and duration variables control patients' pathways through the model, whilst the cost and quality of life variables are attributes linked to particular events or health states that the patient may pass through. The model verification of each group of variables within these categories consisted of entering data into the model for which easily estimated results could be calculated. The expected results from the model, in the incidence and durations category, only reflect the parameter values in terms of their impact on survival. The expected results in costs and QALY's categories reflect the parameters values in the context of the specified incidence and duration values. There are very few sources with which the costs could be validated. Economic evaluations in this area have been limited to modelling exercises (Hillner and Smith, 1993; Smith and Hillner, 1993). However, these studies only
Proceedings of the 24th International Conference on Computers and Industrial Engineering
237
represent costs in terms of incremental costs between the alternative strategies, and give no indication of the length of time over which a patient is followed.
USER r~rERFACE As previously mentioned the data entry and analysis requirements of the ABCSim package led to the development of a user interface as shown in Fig. 1. It was decided by the health economists that the experimentation would be by comparing test patient cohorts with changes in the range of input factors (alternative 1) against a control patients (alternative 2). The main screen provides the user with facilities for accessing different input and output windows (Taylor et al, 1998). The main input factors were age group distribution (see Fig. 2), distribution of treatment path selection, side effects in relation to ovarian suppression and chemotherapy (toxicities), in-trial menopausal distributions, recurrence durations and recurrence type probabilities, distribution of remission durations, costs of different treatments, and quality of life adjusted years. Output is displayed in results sub-windows including average cost, average life years gained, and average quality of life adjusted years. Also provided are the discounted values and confidence intervals (see Fig. 3) for the above results. Some comparative results are also presented such as cost effectiveness ratios and cost-utility ratios. ABCSim also allows users to export the model's results into other packages for further analysis. The main page displays dynamic clock based on years and months and also the number of patients who entered different paths of treatments. Users are able to save the inputs and outputs of each run in a separate file in order to compare them with other configurations later. Fig. 1 ABCSim: main console
Womenrand0misedCTregardlessd +b0 $ F'T RecG~,wd.R°,~,u,,,i~,,r:,dto +OS
Received. Ra~-,do~,i~edto -OS
~T not Received. Randomizedto +OS
not Received. Randomisedto -OS
OS R~c=ived. Rar;do~;,~;.~.ito +CT
R=.,..c;,~d. Rar,d,:,,-,--"#edto -CT
DS not Received. Randomi~eclto +CT
not Received,,Randomi$edto -CT
Post,Meno~au=alRandom~d to +c'r
~t-MenooausaLRandomi=edto
Comparisons of Patients Finished Different Treatment 1.0 0.8 0.6 0.4 0.2 0.0
Alternative i
2
3
4
5
Comparisons
6
7
Alternative2
238
Fig. 2 age distribution input
Fig. 3 confidence intervals screen
CONCLUSIONS This article concludes by suggesting that simulation does not provide a specific answer to the problem as such when it comes to the process of decision making. The real power of simulation modelling lies in its ability to support decision making by allowing those with expertise in the problem domain to play with model and give them the opportunity to explore their problem through the development of a flexible, adaptable, and a visible model, Computer simulation could then be used to predict the system's responses to different alternatives. Further research concentrates on finding ways to generate suitable interfaces to different users based on their backgrounds and the modelling requirements.
The ABCSim project was funded by medical research grant G9437850. REFERENCES Hillner, B. E. and T. J. Smith (1993). Assessing the cost-effectiveness of adjuvant therapies in early breast cancer using a decision analysis model. Breast Cancer Res. and Treat., 24, 97-105. Smith, T. J. and B. E. Hillner (1993). The Efficacy and Cost-Effectiveness of Adjuvant Therapy of Early Breast Cancer in Premenopausal Women. J. Clin. Oncol., 11,771-776. Paul, R. J. and D. W. Balmer (1993). Simulation Modelling. Chartwell-Bratt, Sweden. Pidd, M (1996). Tools for Thinking: Modelling in Management Science. John Wiley & Sons, Chichester. Taylor, S.J.E., T. Eldabi, R.D. Macredie, R.J. Paul, J. Brown and J. Karnon (1998). Economic Evaluation of Adjuvant Breast Cancer Treatment Using Simulation Modelling. In the Proceedings of the 1998 Medical Sciences Simulation Conference. San Diego, California. January 11-14, 1998. The Society for Computer Simulation International, San Diego. 42-47.
onilinilalr'irtil
PERGAMON
Computers & Industrial Engineering 37 (1999) 235-238
COMPUTER SIMULATION IN HEALTHCARE DECISION MAKING Tillal Eldabi Ray J. Paul Simon J. E. Taylor Centre for Applied Simulation Modelling Department of Information Systems and Computing Brunel University, Uxbridge Middlesex UB8 3PH ABSTRACT This paper presents simulation modelling as a decision support technique and suggests that it can be a useful for understanding problems related to health care. The paper shows that simulation may not be regarded as tool for deriving solutions to certain problems. In fact simulation is better suited for understanding the problem and enhancing systematic debate between the problem owners. The paper also demonstrates the usefulness of combining different software to provide a comprehensive tailor-made package (ABCSim). The example used is based on modelling a randomised clinical trial for Adjuvant Breast Cancer. © 1999 Elsevier Science Ltd. All rights reserved. Keywords: simulation modelling, decision making, clinical trials. INTRODUCTION The convoluted nature of health care systems requires managers to use sophisticated decision support techniques. The cost effective allocation of resources is likely to become one of the dominant issues in healthcare as it is in other fields. This article gives an example of how simulation modelling can facilitate informed debate between interested parties and demonstrate the consequences of different possible courses of action. The example discusses ABCSim, a simulation package for health economists with the aim of supporting the economic evaluation of the Adjuvant Breast Cancer (ABC) trial. The trial is a collaborative randomised clinical trial which was in progress in the UK. The principle aims of this trial were: •
•
to determine the value of adding cytotoxic chemotherapy and/or ovarian suppression to prolonged adjuvant tamoxifen in order to treat pre/perimenopausal women with early breast cancer; and to determine the value of adding cytotoxic chemotherapy to prolonged adjuvant tamoxifen in order to treat post menopausal women with early breast cancer.
The choice of treatment is determined by the particular condition of the patient or randomly determined from a series of suitable alternatives. It was proposed that the economic evaluation was to be performed by comparing the additional resource use with the survival gains and quality of life effects of the treatments combinations. However, the huge volume of data that the clinical 0360-8352/99 - see front matter © 1999 Elsevier Science Ltd. All rights reserved. PII: S0360-8352(99)00063-7
236
Proceedings of the 24th International Conference on Computers and Industrial Engineering
trials would generate made this task difficult. Next section provides a discussion about the need and use of modelling in such clinical trials to deal with the data problem. The section that follows presents the stages of modelling followed in this project. The verification and validation process is also visited. The article then gives a brief overview of ABCSim package. M O D E L L I N G THE ECONOMIC FACTORS The main role of simulation in this project is to model the perceived relationships between the economic factors in the treatment of adjuvant breast cancer with respect to reducing the amount of data collection required. Experimentation with the package would then be used to attempt to identify the important variables for data collection and examining the different responses to changes of configurations. The method used in modelling consists of three stages. These stages are problem structuring, modelling, and implementation. Problem structuring attempts to identify the problems in the system under investigation and the approaches that can be used to deal with them. Modelling is the heart of the project and involves the development of, and experimentation with, the computer model. Implementation attempts to put the outcomes of the study into practice. Pidd (1996) identifies the outcomes of these stages as being tangible recommendations or increased insight or understanding. At the first stage of modelling the trial, Activity Cycle Diagram (ACD) was used to generate the basic conceptual structure of the model (Paul and Balmer, 1993). Simul8, a simulation package, was used to generate the computer model based on the ACD model. The choice of the package was based on cost, ease of use and the ease of communicating with other programming tools for building a user interface. The package has special routines to communicate with Visual Basic, which was used for building ABCSim. The design of the interface is based on the user requirements and specification in order to make it easier for them to experiment with model. In other words, Simul8 is used for the modelling process whilst ABCSim is used for input and output manipulation by the users, health economists in this case. VALIDATION AND VERIFICATION The model covers a wide range of input variables that can be split broadly into three distinct categories: incidences and durations, costs, and quality of adjusted life years (QALY's). To date, the study has concentrated on collecting robust data for the validation of the incidence and duration and cost variable categories. The incidence and duration variables control patients' pathways through the model, whilst the cost and quality of life variables are attributes linked to particular events or health states that the patient may pass through. The model verification of each group of variables within these categories consisted of entering data into the model for which easily estimated results could be calculated. The expected results from the model, in the incidence and durations category, only reflect the parameter values in terms of their impact on survival. The expected results in costs and QALY's categories reflect the parameters values in the context of the specified incidence and duration values. There are very few sources with which the costs could be validated. Economic evaluations in this area have been limited to modelling exercises (Hillner and Smith, 1993; Smith and Hillner, 1993). However, these studies only
Proceedings of the 24th International Conference on Computers and Industrial Engineering
237
represent costs in terms of incremental costs between the alternative strategies, and give no indication of the length of time over which a patient is followed.
USER r~rERFACE As previously mentioned the data entry and analysis requirements of the ABCSim package led to the development of a user interface as shown in Fig. 1. It was decided by the health economists that the experimentation would be by comparing test patient cohorts with changes in the range of input factors (alternative 1) against a control patients (alternative 2). The main screen provides the user with facilities for accessing different input and output windows (Taylor et al, 1998). The main input factors were age group distribution (see Fig. 2), distribution of treatment path selection, side effects in relation to ovarian suppression and chemotherapy (toxicities), in-trial menopausal distributions, recurrence durations and recurrence type probabilities, distribution of remission durations, costs of different treatments, and quality of life adjusted years. Output is displayed in results sub-windows including average cost, average life years gained, and average quality of life adjusted years. Also provided are the discounted values and confidence intervals (see Fig. 3) for the above results. Some comparative results are also presented such as cost effectiveness ratios and cost-utility ratios. ABCSim also allows users to export the model's results into other packages for further analysis. The main page displays dynamic clock based on years and months and also the number of patients who entered different paths of treatments. Users are able to save the inputs and outputs of each run in a separate file in order to compare them with other configurations later. Fig. 1 ABCSim: main console
Womenrand0misedCTregardlessd +b0 $ F'T RecG~,wd.R°,~,u,,,i~,,r:,dto +OS
Received. Ra~-,do~,i~edto -OS
~T not Received. Randomizedto +OS
not Received. Randomisedto -OS
OS R~c=ived. Rar;do~;,~;.~.ito +CT
R=.,..c;,~d. Rar,d,:,,-,--"#edto -CT
DS not Received. Randomi~eclto +CT
not Received,,Randomi$edto -CT
Post,Meno~au=alRandom~d to +c'r
~t-MenooausaLRandomi=edto
Comparisons of Patients Finished Different Treatment 1.0 0.8 0.6 0.4 0.2 0.0
Alternative i
2
3
4
5
Comparisons
6
7
Alternative2
238
Fig. 2 age distribution input
Fig. 3 confidence intervals screen
CONCLUSIONS This article concludes by suggesting that simulation does not provide a specific answer to the problem as such when it comes to the process of decision making. The real power of simulation modelling lies in its ability to support decision making by allowing those with expertise in the problem domain to play with model and give them the opportunity to explore their problem through the development of a flexible, adaptable, and a visible model, Computer simulation could then be used to predict the system's responses to different alternatives. Further research concentrates on finding ways to generate suitable interfaces to different users based on their backgrounds and the modelling requirements.
The ABCSim project was funded by medical research grant G9437850. REFERENCES Hillner, B. E. and T. J. Smith (1993). Assessing the cost-effectiveness of adjuvant therapies in early breast cancer using a decision analysis model. Breast Cancer Res. and Treat., 24, 97-105. Smith, T. J. and B. E. Hillner (1993). The Efficacy and Cost-Effectiveness of Adjuvant Therapy of Early Breast Cancer in Premenopausal Women. J. Clin. Oncol., 11,771-776. Paul, R. J. and D. W. Balmer (1993). Simulation Modelling. Chartwell-Bratt, Sweden. Pidd, M (1996). Tools for Thinking: Modelling in Management Science. John Wiley & Sons, Chichester. Taylor, S.J.E., T. Eldabi, R.D. Macredie, R.J. Paul, J. Brown and J. Karnon (1998). Economic Evaluation of Adjuvant Breast Cancer Treatment Using Simulation Modelling. In the Proceedings of the 1998 Medical Sciences Simulation Conference. San Diego, California. January 11-14, 1998. The Society for Computer Simulation International, San Diego. 42-47.