What is the Archimedes Model?
The Archimedes Model is a carefully validated, clinically realistic simulation model of health and healthcare. More...
How do I access the Model?
The Archimedes Model can be accessed as Software as a Service through ARCHeS. This application enables users to run their own healthcare simulations setting up multi-scenario studies, and analyzing the results. More...
Where do you get data for building the Model?
The Archimedes Model is based on data from a variety of sources ranging from basic science studies and epidemiological studies to surveys, clinical data, and clinical trials. For example, models of physiology and diseases are based on basic science studies and clinical trials. Information about the effects of behaviors and risk factors for diseases is obtained from epidemiological studies. Demographic and population data come from large administrative data sets. Information about the effects of treatments is obtained from clinical research. Protocols and guidelines are based on reports issued by national organizations. Information about personnel, facilities, processes of care, performance, and logistics is based on the experiences of existing medical centers. Cost information is drawn from accounting departments.
When building the Model, what do you do when studies show different or conflicting results?
We use standard methods of systematic review to select evidence and determine the strengths of different experimental designs. If two pieces of evidence are both high quality and yet show apparently different results, we examine the details of each to see if the conflicting results can be explained by subtle differences in the populations, treatment protocols, treatment details (e.g. drug dose or administration), definitions of outcomes, follow-up times, compliance rates, or other factors. If so, then we use one study to build the Model, and use the other to validate it.
If there is no clear explanation of the differences or conflicts in results, then we choose the study with the strongest design for the reference case, but note the existence of the conflicting evidence. The importance of the conflicting evidence can then be studied through sensitivity analysis and "what-if" analyses. In this situation, we present results for the conflicting scenarios so decision makers can understand the implications of the uncertainty.
What are the limitations of the Model?
For the conditions that are currently in the Model, the Model includes the most important biomarkers and interventions needed to analyze the great majority of problems. However there may be other conditions, biomarkers, or interventions of interest for a particular problem that are not yet in the Model. These can be added to the Model upon request provided sufficient data are available.
The level of physiological detail of the Model is determined by the level of detail of the questions it is designed to address. Typically this is the level of detail at which clinicians think and make their decisions, the level of detail found in clinical charts, and the level of detail at which clinical trials are designed and reported. For many areas of physiology the causes of clinical phenomena are understood at a deeper level of detail by basic scientists and specialists. In the Archimedes Model these factors are represented phenomenologically, not causally. For example, the Model represents causally the relationships between heart rate, stroke volume, cardiac output, peripheral resistance and blood pressure. But the Model does not represent the effects of cardiac filling on sarcomere length and force of myocardial contraction. If a client is interested in representing this type of phenomenon, the Model can be extended to deeper levels of physiological detail, again provided sufficient data are available.
The ability of the Model to address additional conditions, biomarkers, interventions or physiological processes depends on the available data. In general there must be data that relate any new variables to the existing variables in the Model to which they are causally related. Our validation protocols require that there be data from at least two sources: one for building the equations, and at least one other for performing an independent validation.
How does the Model differ from Markov Models?
A Markov model works by characterizing a person as being in one and only one state at any time. Time is broken into discrete intervals, such as a month or a year. Thus, taking an annual time interval as an example, whatever state a person is in on January 1st, the model will assume the person stays in that state through to December 31st. The progression of a condition is represented by allowing the person to move at midnight on December 31st to a new state. The pace of the progression is determined by "transition probabilities"; for every state there is a probability that on midnight of December 31st, any patient in that state might jump to any of the other states. For example, a person in the "No complication" state might jump to the state "Have CAD".
Before going any further, at least four limitations of the Markov approach are immediately apparent. The first is that the Markov model is not set up to model care processes or system resources. If you want a model to include any of those, you will either have to choose a different type of model, or you will have to append a different type of model to the Markov structure.
The second observation is that the discrete states are usually highly simplified representations of a disease. For example, the state "uncomplicated diabetes" lumps together everyone without complications regardless of the duration of the disease, its severity, what treatments the patient is on, other co-morbidities that patient might have, and so forth. This may be fine if you are only interested in keeping track of the occurrence of states as they are defined, but it is not the right model if your questions are deeper than that.
A third observation has to do with the transition probabilities. The integrity of the entire model rests on them. More specifically, they are being called on to capture and accurately represent everything that might happen to a person during the year, such as changes in the patient's anatomy (e.g. progression of an occlusion in a coronary artery), pathophysiology (e.g. gradual increase in fasting plasma glucose), development of signs and symptoms, patient behaviors in seeking care, visits, workups, and decisions by physicians, performance and interpretation of tests, treatments, actions by patients, effects of treatments, and so forth.
The fourth observation is that time is discrete in a Markov Model. A lot of important problems in healthcare involve time. For example, how frequently should a person be screened? After a patient is put on a drug, when should he or she be brought back for a follow-up visit? How long should a person be on a drug before changing the dose or switching to a different drug? Questions like these are very difficult to address with the discrete Markov structure. By themselves, these four issues rule out the Markov model for problems where more clinical realism is required, or where care processes, logistics, and resources are important.
The Archimedes Model represents the physiological variables that actually underlie the diseases (e.g. insulin resistance, hepatic glucose production, glucose uptake by fat and muscle, plasma glucose). It retains the continuous nature, interactions, and progression of biological variables, and therefore can more accurately describe the complexity of diseases.
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The Archimedes Voluntary Product Accessibility Template (VPAT) describing our adherance to Section 508 can be found at archimedesmodel.com/508-compliance
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