Guidelines for Computer Modeling of Diabetes and Its Complications
- American Diabetes Association Consensus Panel
- Address correspondence and reprint requests to Richard Kahn, PhD, Chief Scientific and Medical Officer, American Diabetes Association, 1701 North Beauregard St., Alexandria, VA 22311. E-mail:
Decision making in medicine relies heavily on clinical studies, preferably randomized controlled clinical trials. Although the evidence obtained from such research is invaluable in guiding health care decisions, this source of information leaves many gaps. The results of a trial are directly applicable only to the population recruited and the protocol used. Few trials (if any) address all the characteristics of the patient population for whom the intervention is appropriate or address multiple interdependent conditions and treatments. Also, clinical trials seldom observe health outcomes over long periods (>5–10 years) and less frequently consider the long-term economic impacts of the interventions.
In the face of these problems, clinicians and policy makers have traditionally had to rely on their judgement. Unfortunately, this also has serious shortcomings, as demonstrated by wide variations in practice patterns, conflicts in guidelines, and high rates of inappropriate care. It is unrealistic to expect the human mind to be able to address the complexity, variability, and uncertainties of health and disease.
To address these important issues, decision makers are increasingly turning to computer modeling as a technology that can provide more informed answers to questions that have not been, or will not be, answered by clinical trials. In the context of this report, a “computer model” is a set of mathematical equations, with algorithms for combining these equations with computer software. There are many different types of models that are applicable to diabetes (1–10) and have been used to address a variety of clinical and economic questions (8–23). If properly constructed, validated, and applied, these models can be very powerful decision-making aids.
As medicine turns to modeling to make more informed decisions, it is imperative that those who develop this technology do so in a way that justifies trust and confidence in the results …