Patient-specific medical simulation holds the promise of evaluating tailored medical treatment based on the particular characteristics of an individual patient and/or an associated pathogen. Furthermore, approaches using simulation are based on the development of theories and models from which deductions can be made, as is the standard approach in the physical sciences and engineering. In reality, biology and medicine are still too poorly understood for deductive approaches to replace inductive ones so, in the foreseeable future, both will continue to sit side by side 1. However for clinical acceptance, verification and validation of these techniques need to be addressed. The patient-specific simulation approach contrasts with more traditional use of computer systems to support clinical decision making, such as ‘classic’ expert systems, which take a Baconian approach, allowing a clinician to infer the cause of symptoms or the efficacy of a particular treatment regime based on historical case data. An example of such a system is the MYCIN expert system 2, designed to suggest possible bacterial causes of a patient’s infection by asking a clinician a series of ‘yes’ or ‘no’ questions.
While the details vary widely between medical conditions, several basic elements are common to all fields of patient-specific medical simulation in support of clinical decision-making. Data is obtained from the patient concerned, for example from an MRI scan or genotypic assay, which is used to construct a computational model. This model is then used to perform a single simulation, or can form the basis of a complex workflow of simulations of a proposed course of treatment; for example, molecular dynamics simulations of drugs interacting with a range of viral proteins, and the results of the simulation are interpreted to assess the efficacy of treatment under consideration. The use a of simulation to assess a range of possible treatments based on data derived from the patient who is to be treated will give the physician the ability to select a treatment based on prior (simulated) knowledge of how the patient will respond to it.
The patient-specific medical simulation scenarios touched on above require access to both appropriate patient data and to the infrastructure on which to perform potentially very large numbers of complex and demanding simulations. Resource providers must furnish access to a wide range of different types of resource, typically made available through a computational grid, and to institute policies that enable the performance of patient-specific simulations on those resources. A computational grid refers to a geographically distributed collection of supercomputing resources, typically connected by high-capacity networking infrastructure, and we define grid computing as distributed computing conducted transparently across multiple administrative domains 3. For the purpose of this article, grids can also include other resources, such as medical imaging equipment and data visualisation facilities.
In order to make patient-specific simulations useful to a physician, results need to be obtained within a clinically useful timeframe, which ranges from instantaneous results to weeks, depending on the scenario. In addition to expediency of access to patient data, consideration must also be given to policy and procedures that ensure maintenance of patient confidentiality. For such an enterprise to succeed, grid computing will need to focus not only on the provision of large ‘island’ compute machines but also on the performance characteristics of the networks connecting them. The process of clinical decision making, requiring access to relevant data, timely availability of computational results, visualisation, data storage, and so on, requires infrastructure that can facilitate the transfer of gigabytes of data within clinically relevant timeframes.
