Pharmacy practice has undergone significant changes in recent years. Pharmacists have transitioned from primarily focusing on medication dispensing to providing personalized care. They are not just drug sellers but integral members of healthcare teams.
This shift has made clinical pharmacy services essential resources in promoting the safe and effective use of medications. Innovations in pharmacy practice often involve complex interventions. Furthermore, the emphasis in this century is on evidence-based care and patient-centered approaches as well as financial considerations.
Undoubtedly, these alterations go along with financial constraints on health care budgets that to be evaluated and taken care of. This is where pharmacoeconomic analysis comes to the rescue. It brings objective insight into the pharmacy industry as it considers the health problems, their solutions, and the monetary burden.
In this article, we provide more information about what pharmacoeconomics is and what tools and software are used in it.
Introduction to pharmacoeconomics
First of all – what exactly is pharmacoeconomics?
Essentially, pharmacoeconomics is a branch of health economics. It evaluates both the costs and benefits of healthcare interventions and compares them to existing alternatives. It can get you information about the cost-effectiveness of the invention and answer the question “Is it worth it?”. This type of analysis is crucial given the limited resources in healthcare.
Pharmacoeconomics is also important for maximizing value for patients and the healthcare system. New interventions – drugs, medical devices, and services, are often more expensive than existing options. However, they may offer additional benefits for the patient but usually lead to financial disturbances. Pharmacoeconomics considers this complicated balance between health and finances.
Full pharmacoeconomic evaluations play a key role in informing coverage and reimbursement decisions. The decision-making in healthcare is an obligation of healthcare providers, policymakers, and stakeholders. They must assess whether these new interventions are cost-effective and represent an efficient use of resources.
Many studies on the economic evaluation of pharmacy services face significant methodological challenges. These studies often fail to meet the standards of comprehensive economic analysis. A common issue is the absence of incremental analysis (i.e., not comparing with an alternative). Moreover, there can be inadequate evaluation of both costs and outcomes and improper assessment of the pharmacy service costs.
Additionally, the complexity of pharmacy services can make it challenging to assign precise costs to each component of the intervention. Last but not least, some outcome measures, such as quality-adjusted life years (QALYs), may not fully capture the value of complex interventions.
It is essential for the field of pharmacy practice to conduct robust economic evaluations of innovative pharmacy services using appropriate methodologies. However, a full economic evaluation is not a single research method. It is a structured framework for addressing specific decision problems.
Researchers must clearly define the aims, target populations, context, and many other components. This clarity ensures that the evidence generated is reliable enough to make informed decisions. [1]
Modeling software in pharmacoeconomics
Modeling software plays a crucial role in conducting economic evaluations of healthcare interventions. These tools help simulate clinical and economic outcomes to assess cost-effectiveness.
Naturally, every software tool uses different models so we will overview the most common ones.
Decision trees
Decision trees are graphical models that predict and map out possible outcomes from an intervention. Exactly like trees, their decision paths branch out into chance nodes and terminal nodes.
Decision trees are useful for evaluating short-term healthcare interventions or comparing treatments on a basic level. For example, you can use a decision tree to choose between two medications for acute pain relief.
This model, however, is not suitable for chronic conditions or long-term analysis due to a lack of cyclic process representation.
Markov models
Markov models, on the other hand, are used to simulate chronic diseases or progressive conditions. They can divide the disease progression into distinct health states (healthy or diseased) and differentiate the cycles the patient does in these states.
For instance, the Markov model is useful for diseases like diabetes, cardiovascular diseases, or cancer. It can assess the long-term cost-effectiveness of particular treatments with consideration of recurring costs and outcomes.
However, this model is not perfect in including past history of illness into consideration.
Discrete event simulation (DES) models
Discrete event simulation (DES) models represent individual patient pathways through a healthcare system over time.
DES tracks individual patients as they experience healthcare events based on specific probabilities and timings. It forms dynamic systems and captures individual variability.
Nevertheless, its limitations are that it requires extensive structured data and computational resources. Moreover, it is more complex to design and interpret compared to simpler models. [2]
Microsoft Excel
Microsoft Excel is one of the most widely used tools in everyday life. Believe it or not, it can be used in pharmacoeconomics for conducting cost-effectiveness modeling (CEM).
Excel supports a wide range of built-in statistical functions. Also, it can be extended through macros for more complex analyses. Users can automate repetitive tasks and create custom functions using Visual Basic for Applications (VBA).
VBA provides a powerful development environment for advanced users. It has syntax highlighting, debugging, and project management tools. Macros can range from basic loops to sophisticated user-defined functions. Excel also offers extensive formatting options. This means that it can create well-structured documents and export them to other Microsoft Office applications like Word and PowerPoint. Open-source alternatives like LibreOffice and OpenOffice offer similar functionalities to Excel.
Excel has faced criticism, particularly regarding the quality of its random number generators (RNGs) and statistical methods. The RNG in Excel remains largely undocumented even in Excel 2016. Typically, using an external RNG or the ‘Randomize’ function in VBA is recommended to enhance reproducibility and validation.
Overall, Excel remains an accessible choice for simpler analyses in pharmacoeconomic modeling.
TreeAge Pro
TreeAge Pro is a widely used visual development tool. It is used for constructing decision-analytic models in Health Technology Assessment (HTA).
TreeAge offers different licensing options tailored to industry needs and specific use cases. For example, users need the Healthcare version to access advanced features like Markov models, micro-simulation, and cost effectiveness analysis.
Additional capabilities such as state-transition diagrams, discrete event simulations (DES), and distributed processing require both the Healthcare module and an active maintenance license. TreeAge’s main strength lies in its user-friendly visual interface.
It allows users to work with influence diagrams or state-transition diagrams that can be converted into decision trees and others. TreeAge can also integrate with various software tools (e.g., Excel). This helps automate the generation of diverse outputs.
MATLAB and R
MATLAB and R are high-level programming languages widely used for mathematical and statistical analyses even in pharmacoeconomics.
Both languages offer powerful features for modeling and analysis but have small differences. They support various types of economic models – decision trees, Markov models, and DES. Furthermore, both languages allow for advanced analyses such as Value of Information (VOI) and Expected Value of Perfect Parameter Information (EVPPI).
Both MATLAB and R can be enhanced with community-developed packages. Those can expand their functionalities for specialized analyses. In addition, both languages feature extensive and customizable plotting options for detailed visualizations of model results.
MATLAB includes its own Integrated Development Environments (IDE). R can be used with popular IDEs like RStudio. These environments provide features like syntax highlighting, error detection, and debugging tools. Both MATLAB and R use the Mersenne Twister as their default RNG.
MATLAB also has built-in functions like Statistics and Machine Learning Toolbox and domain-specific tools like Simulink and SimEvents for the facilitation of DES. MATLAB is largely compatible with Octave, though some differences in syntax, such as RNG seeding methods, exist. MATLAB is a paid software with various licensing options. On the other hand, Octave is free and open-source.
R offers specialized packages like BCEA for Bayesian cost-effectiveness analysis in pharmacoeconomics. Its flexibility allows users to enhance performance through additional packages like Datatable for fast data manipulation and packages that enable parallel computing capabilities. For further optimization, Microsoft R Open offers enhanced math routines and advanced parallelization. R is free and open-source, thus accessible to a broader audience. [3]
Every software tool has its own specifics. It is important to keep them in mind when choosing a tool for a specific type of work.
Economic evaluation software
Modern financial econometrics heavily relies on specialized software packages. They help provide data analysis on the economic impact.
Here is a summary of some widely used econometrics tools:
- EViews is a user-friendly and menu-driven software. It can handle various econometric models. However, it has limited flexibility for advanced model customization. Its ease of use and comprehensive electronic help system are very popular in the pharmacoeconomic world;
- RATS (Regression Analysis of Time Series) is a specialized tool for time series analysis. Users can customize or write their own procedures which adds flexibility for advanced applications. RATS is ideal for time series econometrics and spectral analysis;
- LIMDEP is an econometrics package known for its discrete choice models, data analysis, and efficiency estimation. It offers over 100 built-in estimators for various models. Moreover, LIMDEP is widely adopted in academia, research institutions, and government;
- TSP (Time Series Processor) is an alternative to RATS. It offers a range of modeling tools;
- GAUSS is a fast programming language ideal for intensive computational tasks;
- Mathematica offers extensive mathematical and statistical capabilities. It covers algebra, optimization, and data analysis.
These econometrics packages cater to various needs in financial modeling and statistical analysis. The choice of software depends on the specific requirements of the project, user expertise, and the complexity of the econometric models involved. [4]
Pharmacoeconomic data analysis elements and terms
The pharmacoeconomic analysis involves considering the cost-effectiveness of pharmaceutical interventions. This means integrating clinical outcomes with economic considerations.
There are some key components used in economic evaluations in healthcare. Economic evaluation requires comparing at least two health interventions to assess both their costs and consequences.
Here we present some health economic modeling terms that are used to describe and compare interventions:
- The target population is a term for the specific group of patients who are expected to benefit from the intervention.
- Comparators are the alternative interventions (e.g., drugs, treatments, services) used for comparison.
- The setting is the environment where the intervention will take place (hospital, outpatient clinic, or community setting).
- The viewpoint from which costs and benefits are assessed is called perspective. It can be the perspective of the patient, healthcare provider, payer (insurer), or society as a whole.
- The time horizon is the duration over which costs and outcomes are measured. It could be short-term (months) or long-term (years or lifetime).
- Opportunity costs represent the potential benefits lost when choosing one intervention over another.
- Costs are the finances involved in the analysis. They are categorized into direct, indirect, and intangible. Direct costs are the medical expenses (medications, hospital stays) and non-medical costs (transportation) related to the intervention. Indirect costs are the losses in productivity due to illness or time taken off work. Intangible costs are related to pain, suffering, or decreased quality of life.
- Cost of Illness analysis (COI) estimates the total financial burden of a specific disease or condition on society. It includes all types of costs related to the disorder.
- Cost benefit analysis (CBA) is a method used to compare costs to its benefits. The goal is to determine whether the benefits of a project or decision outweigh the costs.
- Outcomes are the benefits or consequences of the intervention. These are the expected health results from an intervention. Expected results are, for example, improved quality of life or reduced mortality.
- Willingness to pay (WTP) refers to the maximum amount individuals are willing to pay for particular health benefits.
- Discounting is a technique to adjust future costs and benefits to their present value.
- Modeling is a process of the use of decision-analytic models to represent complex healthcare scenarios and predict outcomes.
- Sensitivity analysis examines the uncertainty in the results by varying key parameters. One-way sensitivity analysis varies one parameter at a time. Multiway sensitivity analysis varies multiple parameters simultaneously.
- Threshold analysis identifies the point at which the decision changes.
- Probabilistic sensitivity analysis uses distributions for uncertain parameters to evaluate overall uncertainty.
All of these parameters are used to measure drug costs and outcomes. They provide quality and quantity analysis of the interventions. These terms structure pharmacoeconomic models, thereby guiding decisions on drug pricing and reimbursement policies.
The choice of pharmacoeconomic analysis software depends on the complexity of the model, the user’s programming skills, and specific project requirements. Sometimes, having a custom-developed solution is the best way to analyze and optimize the healthcare resources in the long run.
Decision-analytic software in pharmacoeconomics
Decision-analytic software for pharmacoeconomics unites tools and techniques for creating and analyzing models in healthcare. These tools support decision-making by simulating various healthcare scenarios and comparing treatment options. In the meantime, healthcare decision analysis can be implemented using various modeling techniques.
An example of that is the already explained decision trees. Also, there are simulation models like discrete event simulation and state-transition models.
DES is effective in settings like emergency care or infectious disease transmission. These events must be mutually exclusive (e.g., transitioning from a waiting room to an exam room). DES is valuable for analyzing systems with constrained resources but can be complex to develop and implement.
On the other hand, state-transition models represent changes in health states over time. They can be used in two main forms.
The first model is the Markov Cohort Model, which evaluates a group of patients (cohort) by tracking transitions between health states based on predefined probabilities.
The second model, microsimulation (or First-order Monte Carlo Model), focuses on individual patient trajectories. It is particularly effective for assessing long-term interventions, screening programs, and treatment strategies.
All healthcare decision analyses share common features. They require defining the clinical context, time frame, and target patient population.
Ultimately, decision analyses utilize transition probabilities to estimate the likelihood of patients moving between health states. They assess costs, health effects, or both, to evaluate the outcomes of different decisions. Inputs often come from multiple studies, combining evidence on intervention efficacy, costs, and patient outcomes.[6]
Budget impact analysis software
Budget Impact Analysis (BIA) is a financial assessment method. It is used to consider adopting a new healthcare intervention or technology on a specific healthcare budget. BIA centers purely on the financial impact and affordability of the intervention. Typically it is conducted from the payer’s perspective, such as insurance companies.
The analysis generally considers a short-term time horizon (3-5 years). It examines different cost categories (e.g., treatment costs, monitoring, and administration) and potential changes in healthcare utilization.
BIA helps in decision-making for financial benefits. Moreover, it provides input for pricing negotiations and reimbursement decisions. BIA also assists in decisions about including new technologies in formularies by illustrating the short-term financial impact on budgets. Budget Impact Model Tools are part of Microsoft Excel, TreeAge Pro, Python, and others.
Why is this important for healthcare? BIA provides quantitative evidence for pricing. It is used in reimbursement decision-making. It also demonstrates the affordability of new treatments and can guide immediate budget allocations if needed.
In general, BIA software streamlines analyses to support healthcare innovation and its implications. [5]
Cost-effectiveness analysis tools
Cost-Effectiveness Analysis (CEA) is a widely utilized method in economic evaluations. It compares the costs and health outcomes of different healthcare interventions.
According to the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), CEA evaluates interventions in a specific way. Also, it assesses their costs in monetary terms and their outcomes in non-monetary health units. These outcomes may include measures such as reduced mortality, lower morbidity, life years gained, and others.
CEA focuses on the value of health outcomes relative to costs. This differentiates it from BIA because BIA focuses only on the financial part of pharmacoeconomics. Also, CEAs typically consider a long-term time horizon to capture the extended effects of healthcare interventions. This time frame is often based on data from clinical trials and uses forecasting methods to predict future outcomes. Inputs often represent population averages to generalize the findings.
CEA calculates the Incremental Cost-Effectiveness Ratios (ICER). The ICER is determined by dividing the difference in costs (incremental cost) by the difference in health outcomes (incremental effect). It reflects the additional cost per unit of health benefit gained. Cost-Effectiveness Analysis provides a structured approach to evaluate the economic value of healthcare interventions. Established software such as TreeAge Pro, Stata, and R include CEA tools.
CEA tools work beneficial for healthcare in many aspects. They define decision pathways and input costs. CEA software visualizes cost-effectiveness planes, acceptability curves, and threshold analyses. It provides results in formats suitable for policy or reimbursement submissions. Certainly, all of this potential can be used to maximize health benefits within budget constraints. [5]
Software for Markov modeling in pharmacoeconomics
Markov models are commonly used to simulate the progression of chronic diseases over time, particularly useful in CEA. These models represent health processes where individuals transition between different health states. They can be any clinical condition such as “well,” “disease,” or “dead” grouped in cycles. At the end of each cycle (a fixed time period), individuals may stay in their current state or move to another. Each health state is associated with specific costs and utilities.
There are a number of key steps for conducting the Markov model:
- Identify all possible health states and allowable transitions between them.
- After that, determine an appropriate duration for each cycle.
- Specify the likelihood of moving from one state to another at the end of each cycle and the associated economic costs and health outcomes with each state.
- Next, use simulation techniques to evaluate long-term outcomes and costs.
Software that works with the Markov model are MaCS (Markov Chain Simulation), Simul8, and others.
Tools like TreeAge Pro and Simul8 offer user-friendly work with Markov modeling. Their open-source options handle large data which makes them suitable for complex analysis.
Markov modeling software generates results such as cumulative costs, QALYs, and state transition probabilities. This makes it useful for evaluating chronic disease interventions, preventive measures, and new pharmaceutical therapies. [6]
Bayesian analysis software
Bayesian analysis is a statistical inference method that combines prior knowledge with new evidence to make informed conclusions. This approach begins by specifying a prior probability distribution for a parameter of interest.
Bayes’s theorem is applied to integrate the prior information with the evidence. This results in a posterior probability distribution. This posterior distribution forms the basis for drawing statistical inferences about the parameter. Markedly, Bayesian analysis is widely used in various fields including pharmacoeconomics. [7]
Bayesian analysis can integrate various data sources. This makes it useful to present scenarios that are complex. Moreover, Bayesian analysis helps quantify uncertainty and variability in model parameters. The disadvantage of this method is that it requires expertise. Also, results are sensitive to the choice of prior distributions. Several software tools support Bayesian analysis in pharmacoeconomics. These include WinBUGS/OpenBUGS, R, and others.
WinBUGS/OpenBUGS requires familiarity with Bayesian modeling and syntax. It is often used alongside R for data pre-processing and result visualization. R (with Bayesian Libraries) is suitable for integrating with other pharmacoeconomic tools (e.g., cost-effectiveness models). JAGS (Just Another Gibbs Sampler) is frequently used for hierarchical models and meta-analyses.
Bayesian analysis software defines the object of research, e.g. cost-effectiveness comparison of two treatments. Then it combines trial data, real-world evidence, or expert opinions for visualization of the results. This means that Bayesian analysis provides actionable insights that enable informed decision-making.[8]
Health economic modeling software
Health economic modeling is a critical process for comparing the costs and outcomes of various healthcare interventions. It helps decision-makers optimize treatment strategies, policy development, and resource allocation. The most common models used include cost-effectiveness models (CEMs), which assess the value of different interventions in terms of costs and specific health outcomes.
Also popular are the Markov models, budget impact models (BIMs), and others. Consulting firms provide expertise in developing these models to meet HTA (Health Technology Assessment) requirements and ensure that products demonstrate economic value effectively.
Choosing the right health economic software depends on several factors. Key considerations include model type, ease of use, flexibility, etc. You should also consider if you can integrate with other data sources or software you use.
Implementation of health economic modeling software has its benefits. It can affect policies and reimbursement decisions due to its analysis of the monetary burden. The software helps to prioritize healthcare interventions based on cost-effectiveness and affordability. Moreover, such software helps the manufacturers, too. It supports them in decisions for pricing, market access, and demonstration of economic value. [9]
Discover how we can help outsource Healthcare projects efficiently Speak to an expert today, and see how our on-demand IT talent and augmented teams can efficiently deliver value at every step of your roadmap.
Pharmacoeconomic software integration and implementation
Pharmacoeconomics in pharmacy services guides clinical decisions and shapes policies. Pharmacists now deliver non-dispensing services, including chronic disease management, vaccinations, and wellness programs.
Sustaining these services requires the development of effective payment models. Researchers often design economic evaluations poorly or omit important details. Future studies must improve methods and create reliable frameworks to measure the economic value of pharmacist services.
The typical algorithm of pharmacoeconomic software implementation involves:
- Defining the objective – identifying the purpose (e.g., CEA, BIA);
- Data collecting – gathering clinical, cost, and utility data;
- Model selection – choosing the appropriate model type (e.g., Markov, simulation);
- Input parameters – input of relevant variables, such as transition probabilities, cost parameters, and health outcomes;
- Run analysis and sensitivity analysis;
- Data interpretation – analysis of results for decision-making and policy recommendations.
This algorithm ensures the integration, accuracy, and usability of pharmacoeconomics tools for healthcare decision-making. [5]
In conclusion, pharmacoeconomic software tools are essential for evaluating the overall value of healthcare interventions. These tools enable decision-makers to assess various treatment options.
Furthermore, they improve resource allocation, support reimbursement strategies, and ensure sustainable healthcare practices. Selecting the right software and tools requires careful consideration.
Undoubtedly, pharmacoeconomic tools will continue to play a critical role in shaping healthcare policies and improving patient outcomes in the future.
Sources
[1] Tonin, Fernanda S et al. “Principles of pharmacoeconomic analysis: the case of pharmacist-led interventions.” Pharmacy practice vol. 19,1 (2021): 2302. doi:10.18549/PharmPract.2021.1.2302
[2] Stahl, James E. “Modelling methods for pharmacoeconomics and health technology assessment: an overview and guide.” PharmacoEconomics vol. 26,2 (2008): 131-48. doi:10.2165/00019053-200826020-00004
[3] Hollman, Chase et al. “A Comparison of Four Software Programs for Implementing Decision Analytic Cost-Effectiveness Models.” PharmacoEconomics vol. 35,8 (2017): 817-830. doi:10.1007/s40273-017-0510-8
[5] Tonin, Fernanda S et al. “Principles of pharmacoeconomic analysis: the case of pharmacist-led interventions.” Pharmacy practice vol. 19,1 (2021): 2302. doi:10.18549/PharmPract.2021.1.2302
[6]Thomas, D., Hiligsmann, M., John, D., Al Ahdab, O. G., & Li, H. (2019). Pharmacoeconomic Analyses and Modeling. Clinical Pharmacy Education, Practice and Research, 261–275. doi:10.1016/b978-0-12-814276-9.00018-0
[7] https://www.britannica.com/science/Bayesian-analysis
[8]Shih, Ya-Chen Tina. “Bayesian approach in pharmacoeconomics: relevance to decision-makers.” Expert review of pharmacoeconomics & outcomes research vol. 3,3 (2003): 237-50. doi:10.1586/14737167.3.3.237
